"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TFDebertaVaForMaskedLM,
       TFDebertaVaForQuestionAnswering,
       TFDebertaVaForSequenceClassification,
       TFDebertaVaForTokenClassification,
       TFDebertaVaModel,
   )






class  snake_case__      :


 def __init__( self    ,		lowerCamelCase    ,		lowerCamelCase=13    ,		lowerCamelCase=7    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=99    ,		lowerCamelCase=32    ,		lowerCamelCase=2    ,		lowerCamelCase=4    ,		lowerCamelCase=37    ,		lowerCamelCase="gelu"    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=512    ,		lowerCamelCase=16    ,		lowerCamelCase=2    ,		lowerCamelCase=0.02    ,		lowerCamelCase=False    ,		lowerCamelCase=True    ,		lowerCamelCase="None"    ,		lowerCamelCase=3    ,		lowerCamelCase=4    ,		lowerCamelCase=None    ,		):
        __a       =    parent
        __a       =    batch_size
        __a       =    seq_length
        __a       =    is_training
        __a       =    use_input_mask
        __a       =    use_token_type_ids
        __a       =    use_labels
        __a       =    vocab_size
        __a       =    hidden_size
        __a       =    num_hidden_layers
        __a       =    num_attention_heads
        __a       =    intermediate_size
        __a       =    hidden_act
        __a       =    hidden_dropout_prob
        __a       =    attention_probs_dropout_prob
        __a       =    max_position_embeddings
        __a       =    type_vocab_size
        __a       =    type_sequence_label_size
        __a       =    initializer_range
        __a       =    num_labels
        __a       =    num_choices
        __a       =    relative_attention
        __a       =    position_biased_input
        __a       =    pos_att_type
        __a       =    scope


 def 						a__   ( self						):
        __a       =    ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size						)

        __a       =    None
        if self.use_input_mask:
               __a       =    random_attention_mask([self.batch_size, self.seq_length]						)

        __a       =    None
        if self.use_token_type_ids:
               __a       =    ids_tensor([self.batch_size, self.seq_length]    ,		self.type_vocab_size						)

        __a       =    None
        __a       =    None
        __a       =    None
        if self.use_labels:
               __a       =    ids_tensor([self.batch_size]    ,		self.type_sequence_label_size						)
               __a       =    ids_tensor([self.batch_size, self.seq_length]    ,		self.num_labels						)

        __a       =    DebertaVaConfig(
            vocab_size=self.vocab_size    ,		hidden_size=self.hidden_size    ,		num_hidden_layers=self.num_hidden_layers    ,		num_attention_heads=self.num_attention_heads    ,		intermediate_size=self.intermediate_size    ,		hidden_act=self.hidden_act    ,		hidden_dropout_prob=self.hidden_dropout_prob    ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,		max_position_embeddings=self.max_position_embeddings    ,		type_vocab_size=self.type_vocab_size    ,		relative_attention=self.relative_attention    ,		position_biased_input=self.position_biased_input    ,		initializer_range=self.initializer_range    ,		return_dict=lowerCamelCase    ,		)

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    TFDebertaVaModel(config=lowerCamelCase						)
        __a       =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

        __a       =    [input_ids, input_mask]
        __a       =    model(lowerCamelCase						)

        __a       =    model(lowerCamelCase						)

        self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    TFDebertaVaForMaskedLM(config=lowerCamelCase						)
        __a       =    {
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }
        __a       =    model(lowerCamelCase						)
        self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.seq_length, self.vocab_size)						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    self.num_labels
        __a       =    TFDebertaVaForSequenceClassification(config=lowerCamelCase						)
        __a       =    {
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }

        __a       =    model(lowerCamelCase						)
        self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels)						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    self.num_labels
        __a       =    TFDebertaVaForTokenClassification(config=lowerCamelCase						)
        __a       =    {
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }
        __a       =    model(lowerCamelCase						)
        self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.seq_length, self.num_labels)						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    TFDebertaVaForQuestionAnswering(config=lowerCamelCase						)
        __a       =    {
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }

        __a       =    model(lowerCamelCase						)
        self.parent.assertEqual(result.start_logits.shape    ,		(self.batch_size, self.seq_length)						)
        self.parent.assertEqual(result.end_logits.shape    ,		(self.batch_size, self.seq_length)						)






 def 						a__   ( self						):
        __a       =    self.prepare_config_and_inputs()
        (
            (
            __a
        ) ,   (
            __a
        ) ,   (
            __a
        ) ,   (
            __a
        ) ,   (
            __a
        ) ,   (
            __a
        ) ,   (
            __a
        ) ,   
        )       =    config_and_inputs
        __a       =    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
        return config, inputs_dict






@require_tf
class  snake_case__      (       snake_case_,						snake_case_,						unittest.TestCase       ):
 _snake_case       :					Optional[Any]							  =						(
     (
         TFDebertaVaModel,
         TFDebertaVaForMaskedLM,
         TFDebertaVaForQuestionAnswering,
         TFDebertaVaForSequenceClassification,
         TFDebertaVaForTokenClassification,
     )
     if is_tf_available()
     else ()
 )
 _snake_case       :					List[Any]							  =						(
     {
         """feature-extraction""": TFDebertaVaModel,
         """fill-mask""": TFDebertaVaForMaskedLM,
         """question-answering""": TFDebertaVaForQuestionAnswering,
         """text-classification""": TFDebertaVaForSequenceClassification,
         """token-classification""": TFDebertaVaForTokenClassification,
         """zero-shot""": TFDebertaVaForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )

 _snake_case       :					Optional[Any]							  =						False
 _snake_case       :					List[Any]							  =						False


 def 						a__   ( self						):
        __a       =    TFDebertaVaModelTester(self						)
        __a       =    ConfigTester(self    ,		config_class=lowerCamelCase    ,		hidden_size=37						)


 def 						a__   ( self						):
        self.config_tester.run_common_tests()


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowerCamelCase						)






 @slow
 def 						a__   ( self						):
        __a       =    TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"						)
        self.assertIsNotNone(lowerCamelCase						)






@require_tf
class  snake_case__      (       unittest.TestCase       ):


 @unittest.skip(reason="Model not available yet"						)
 def 						a__   ( self						):
        pass






 @slow
 def 						a__   ( self						):
        __a       =    TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"						)
        __a       =    tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]						)
        __a       =    tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
        __a       =    model(lowerCamelCase    ,		attention_mask=lowerCamelCase						)[0]

        __a       =    tf.constant(
            [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]						)
        tf.debugging.assert_near(output[:, 1:4, 1:4]    ,		lowerCamelCase    ,		atol=1E-4						)

"""simple docstring"""



import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

SCREAMING_SNAKE_CASE__:Union[str, Any]									=			2






class  snake_case__      :


 def __init__( self    ,		*,  # begin keyword-only arguments
     lowerCamelCase="<s>"    ,		lowerCamelCase="<pad>"    ,		lowerCamelCase="</s>"    ,		lowerCamelCase="<unk>"    ,		lowerCamelCase=None    ,		):
        __a ,   __a ,   __a ,   __a       =    bos, unk, pad, eos
        __a       =    []
        __a       =    []
        __a       =    {}
        __a       =    self.add_symbol(lowerCamelCase						)
        __a       =    self.add_symbol(lowerCamelCase						)
        __a       =    self.add_symbol(lowerCamelCase						)
        __a       =    self.add_symbol(lowerCamelCase						)
        if extra_special_symbols:
               for s in extra_special_symbols:
                      self.add_symbol(lowerCamelCase						)
        __a       =    len(self.symbols						)


 def __eq__( self    ,		lowerCamelCase						):
        return self.indices == other.indices


 def __getitem__( self    ,		lowerCamelCase						):
        if idx < len(self.symbols						):
               return self.symbols[idx]
        return self.unk_word


 def __len__( self						):
        return len(self.symbols						)


 def __contains__( self    ,		lowerCamelCase						):
        return sym in self.indices


 @classmethod
 def 						a__   ( cls    ,		lowerCamelCase						):
        __a       =    cls()
        d.add_from_file(lowerCamelCase						)
        return d


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase=1    ,		lowerCamelCase=False						):
        if word in self.indices and not overwrite:
               __a       =    self.indices[word]
               __a       =    self.count[idx] + n
               return idx
        else:
               __a       =    len(self.symbols						)
               __a       =    idx
               self.symbols.append(lowerCamelCase						)
               self.count.append(lowerCamelCase						)
               return idx


 def 						a__   ( self    ,		lowerCamelCase						):
        return 0






 def 						a__   ( self    ,		lowerCamelCase						):
        if isinstance(lowerCamelCase    ,		lowerCamelCase						):
               try:
                      with open(lowerCamelCase    ,		"r"    ,		encoding="utf-8"						) as fd:
                             self.add_from_file(lowerCamelCase						)
               except FileNotFoundError as fnfe:
                      raise fnfe
               except UnicodeError:
                      raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(lowerCamelCase						)						)
               return

        __a       =    f.readlines()
        __a       =    self._load_meta(lowerCamelCase						)

        for line in lines[indices_start_line:]:
               try:
                      __a ,   __a       =    line.rstrip().rsplit(" "    ,		1						)
                      if field == "#fairseq:overwrite":
                             __a       =    True
                             __a ,   __a       =    line.rsplit(" "    ,		1						)
                      else:
                             __a       =    False
                      __a       =    int(lowerCamelCase						)
                      __a       =    line
                      if word in self and not overwrite:
                             raise RuntimeError(
                                 "Duplicate word found when loading Dictionary: '{}'. "
                                 "Duplicate words can overwrite earlier ones by adding the "
                                 "#fairseq:overwrite flag at the end of the corresponding row "
                                 "in the dictionary file. If using the Camembert model, please "
                                 "download an updated copy of the model file.".format(lowerCamelCase						)						)
                      self.add_symbol(lowerCamelCase    ,		n=lowerCamelCase    ,		overwrite=lowerCamelCase						)
               except ValueError:
                      raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'"						)



def 	_lowerCamelCase(	a				):
       # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
       # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
       __a       =    dict((re.sub(R"@@$"   ,    ""   ,    a				), v) if k.endswith("@@"				) else (re.sub(R"$"   ,    "</w>"   ,    a				), v) for k, v in d.items()				)
       __a       =    "<s> <pad> </s> <unk>".split()
       # restore the special tokens
       for k in keep_keys:
              del da[F"{k}</w>"]
              __a       =    d[k]  # restore
       return da



def 	_lowerCamelCase(	a   ,    a				):
       # prep
       if not os.path.exists(a				):
              raise ValueError(F"path {biogpt_checkpoint_path} does not exist!"				)
       os.makedirs(a   ,    exist_ok=a				)
       print(F"Writing results to {pytorch_dump_folder_path}"				)

       # handle various types of models

       __a       =    os.path.join(a   ,    "checkpoint.pt"				)
       if not os.path.isfile(a				):
              raise ValueError(F"path to the file {checkpoint_file} does not exist!"				)
       __a       =    torch.load(a   ,    map_location="cpu"				)

       __a       =    chkpt["cfg"]["model"]

       # dicts
       __a       =    os.path.join(a   ,    "dict.txt"				)
       if not os.path.isfile(a				):
              raise ValueError(F"path to the file {dict_file} does not exist!"				)
       __a       =    Dictionary.load(a				)
       __a       =    rewrite_dict_keys(src_dict.indices				)
       __a       =    len(a				)
       __a       =    os.path.join(a   ,    VOCAB_FILES_NAMES["vocab_file"]				)
       print(F"Generating {src_vocab_file} of {src_vocab_size} records"				)
       with open(a   ,    "w"   ,    encoding="utf-8"				) as f:
              f.write(json.dumps(a   ,    ensure_ascii=a   ,    indent=a				)				)

       # merges_file (bpecodes)
       __a       =    os.path.join(a   ,    "bpecodes"				)
       if not os.path.isfile(a				):
              raise ValueError(F"path to the file {bpecodes_file} does not exist!"				)

       __a       =    os.path.join(a   ,    VOCAB_FILES_NAMES["merges_file"]				)
       shutil.copyfile(a   ,    a				)

       # model config
       __a       =    os.path.join(a   ,    "config.json"				)

       __a       =    {
           "activation_dropout": args["activation_dropout"],
           "architectures": ["BioGptForCausalLM"],
           "attention_probs_dropout_prob": args["attention_dropout"],
           "bos_token_id": 0,
           "eos_token_id": 2,
           "hidden_act": args["activation_fn"],
           "hidden_dropout_prob": args["dropout"],
           "hidden_size": args["decoder_embed_dim"],
           "initializer_range": 0.02,
           "intermediate_size": args["decoder_ffn_embed_dim"],
           "layer_norm_eps": 1E-1_2,
           "layerdrop": args["decoder_layerdrop"],
           "max_position_embeddings": args["max_target_positions"],
           "model_type": "biogpt",
           "num_attention_heads": args["decoder_attention_heads"],
           "num_hidden_layers": args["decoder_layers"],
           "pad_token_id": 1,
           "scale_embedding": not args["no_scale_embedding"],
           "tie_word_embeddings": args["share_decoder_input_output_embed"],
           "vocab_size": src_vocab_size,
       }

       # good hparam defaults to start with

       print(F"Generating {biogpt_model_config_file}"				)
       with open(a   ,    "w"   ,    encoding="utf-8"				) as f:
              f.write(json.dumps(a   ,    ensure_ascii=a   ,    indent=a				)				)

       # tokenizer config
       __a       =    os.path.join(a   ,    a				)

       __a       =    {
           "bos_token": "<s>",
           "eos_token": "</s>",
           "model_max_length": 1_0_2_4,
           "pad_token": "<pad>",
           "special_tokens_map_file": None,
           "tokenizer_class": "BioGptTokenizer",
           "unk_token": "<unk>",
       }

       print(F"Generating {biogpt_tokenizer_config_file}"				)
       with open(a   ,    "w"   ,    encoding="utf-8"				) as f:
              f.write(json.dumps(a   ,    ensure_ascii=a   ,    indent=a				)				)

       # model
       __a       =    chkpt["model"]

       # remove unneeded keys
       __a       =    [
           "decoder.version",
       ]
       for k in ignore_keys:
              model_state_dict.pop(a   ,    a				)

       __a       =    list(model_state_dict.keys()				)
       for layer_name in layer_names:
              if layer_name.endswith("output_projection.weight"				):
                     __a       =    model_state_dict.pop(a				)
              else:
                     __a       =    model_state_dict.pop(a				)

       __a       =    BioGptConfig.from_pretrained(a				)
       __a       =    BioGptForCausalLM(a				)

       # check that it loads ok
       model_new.load_state_dict(a				)

       # save
       __a       =    os.path.join(a   ,    a				)
       print(F"Generating {pytorch_weights_dump_path}"				)
       torch.save(a   ,    a				)

       print("Conversion is done!"				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Any									=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--biogpt_checkpoint_path""",
       default=None,
       type=str,
       required=True,
       help=(
           """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
           """ bpecodes, etc."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   SCREAMING_SNAKE_CASE__:Any									=			parser.parse_args()
   convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)
